{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# 导入必要包\n",
    "import cv2\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def readimg(filename, mode):\n",
    "    # 解决中文路径问题\n",
    "    raw_data = np.fromfile(filename, dtype=np.uint8)\n",
    "    img = cv2.imdecode(raw_data, mode)\n",
    "    return img"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_file_dir = './images/行书/丙/敬世江_5945d30c02c1e30a89de9dc3920a0011adc9aa46.jpg'\n",
    "img = readimg(test_file_dir,-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x23943faeb50>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "from skimage.feature import hog\n",
    "from skimage import data, exposure\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def resizeGray(img,new_size):\n",
    "    img = cv2.resize(img,new_size)\n",
    "    img = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)\n",
    "    return img\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "image = resizeGray(img,(200,200))\n",
    "\n",
    "fd, hog_image = hog(image, orientations=4, pixels_per_cell=(16, 16),\n",
    "                    cells_per_block=(1, 1), visualize=True)\n",
    "\n",
    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(8, 4), sharex=True, sharey=True)\n",
    "\n",
    "ax1.axis('off')\n",
    "ax1.imshow(image, cmap=plt.cm.gray)\n",
    "ax1.set_title('Input image')\n",
    "\n",
    "# Rescale histogram for better display\n",
    "hog_image_rescaled = exposure.rescale_intensity(hog_image, in_range=(0, 10))\n",
    "\n",
    "ax2.axis('off')\n",
    "ax2.imshow(hog_image_rescaled, cmap=plt.cm.gray)\n",
    "ax2.set_title('Histogram of Oriented Gradients')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import glob\n",
    "import random"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 列出目录下的文件\n",
    "def listdir_nohidden(path):\n",
    "    return glob.glob(os.path.join(path, '*'))\n",
    "\n",
    "# 读取文件\n",
    "def image_reader(file_name,new_size):\n",
    "    img = readimg(file_name,-1)\n",
    "    img = resizeGray(img,new_size)\n",
    "    return img\n",
    "\n",
    "# 从文件中读取特征和标签\n",
    "def get_file_hog_label_list_from_disk(selectNum=1000):\n",
    "    char_styles = ['篆书','隶书','草书','行书','楷书']\n",
    "    # 特征列表和标签列表\n",
    "    fileFeaturesList = []\n",
    "    fileLabelList = []\n",
    "    # 遍历各种风格\n",
    "    for style in char_styles:\n",
    "        file_list = glob.glob('./images/'+style+'/*/*')\n",
    "        print('风格：{style}下共有{num}张图片\\n'.format(style=style,num = len(file_list)))\n",
    "        # 打乱顺序\n",
    "        random.shuffle(file_list)\n",
    "        # 挑选固定数量文件\n",
    "        select_files = file_list[:selectNum]\n",
    "        \n",
    "        # 挑选指定数量文件\n",
    "        for file_item in select_files:\n",
    "            # 读取文件\n",
    "            img = image_reader(file_item,(100,100))\n",
    "            # 提取特征\n",
    "            features = hog(img, orientations=4, pixels_per_cell=(6,6),cells_per_block=(2,2))\n",
    "            features = list(features)\n",
    "\n",
    "            fileFeaturesList.append(features)\n",
    "            fileLabelList.append(char_styles.index(style))\n",
    "            \n",
    "        print('风格：{style}，共挑选了{num}张图片\\n\\n'.format(style=style,num = len(select_files)))\n",
    "        \n",
    "                \n",
    "    return fileFeaturesList,fileLabelList\n",
    "\n",
    "           "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn import svm\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import confusion_matrix\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "风格：篆书下共有9928张图片\n",
      "\n",
      "风格：篆书，共挑选了9000张图片\n",
      "\n",
      "\n",
      "风格：隶书下共有18421张图片\n",
      "\n",
      "风格：隶书，共挑选了9000张图片\n",
      "\n",
      "\n",
      "风格：草书下共有42923张图片\n",
      "\n",
      "风格：草书，共挑选了9000张图片\n",
      "\n",
      "\n",
      "风格：行书下共有43452张图片\n",
      "\n",
      "风格：行书，共挑选了9000张图片\n",
      "\n",
      "\n",
      "风格：楷书下共有20686张图片\n",
      "\n",
      "风格：楷书，共挑选了9000张图片\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# 每个字最多挑选1个\n",
    "fileFeaturesList,fileLabelList = get_file_hog_label_list_from_disk(selectNum=1000)\n",
    "\n",
    "\n",
    "# 将样本分为训练和测试样本\n",
    "x_train,x_test,y_train,y_test  = train_test_split(fileFeaturesList,fileLabelList,\n",
    "                                                  test_size=0.25,random_state=42)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Counter({0: 9000, 1: 9000, 2: 9000, 3: 9000, 4: 9000})"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 统计各种类别数量\n",
    "from collections import Counter\n",
    "Counter(fileLabelList)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "45000"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(fileLabelList)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "svm acc:0.8216888888888889\n",
      "svm 1 acc:0.7407111111111111\n",
      "svm  2 acc:0.8414222222222222\n",
      "KNN acc:0.7395555555555555\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# SVM分类器\n",
    "cls = svm.SVC(kernel='rbf')\n",
    "cls.fit(x_train,y_train)\n",
    "predictLabels = cls.predict(x_test)\n",
    "\n",
    "print ( \"svm acc:%s\" % accuracy_score(y_test,predictLabels))\n",
    "\n",
    "cls1 = svm.SVC(kernel='linear')\n",
    "cls1.fit(x_train,y_train)\n",
    "predictLabels = cls1.predict(x_test)\n",
    "\n",
    "print ( \"svm 1 acc:%s\" % accuracy_score(y_test,predictLabels))\n",
    "\n",
    "\n",
    "cls2 = svm.SVC(kernel='poly')\n",
    "cls2.fit(x_train,y_train)\n",
    "predictLabels = cls2.predict(x_test)\n",
    "\n",
    "print ( \"svm  2 acc:%s\" % accuracy_score(y_test,predictLabels))\n",
    "\n",
    "\n",
    "\n",
    "# KNN\n",
    "neigh = KNeighborsClassifier(n_neighbors=3)\n",
    "neigh.fit(x_train,y_train)\n",
    "predictLabels = neigh.predict(x_test)\n",
    "print  (\"KNN acc:%s\" % accuracy_score(y_test,predictLabels))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['./models/neigh.joblib']"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 保存模型\n",
    "from joblib import dump, load\n",
    "dump(cls, './models/svc.joblib') \n",
    "dump(neigh, './models/neigh.joblib') \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "svm acc:0.8216888888888889\n"
     ]
    }
   ],
   "source": [
    "# 调用模型\n",
    "cls = load('./models/svc.joblib') \n",
    "predictLabels = cls.predict(x_test)\n",
    "\n",
    "print ( \"svm acc:%s\" % accuracy_score(y_test,predictLabels))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "cm = confusion_matrix(y_test, predictLabels)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sn\n",
    "import pandas as pd\n",
    "\n",
    "df_cm = pd.DataFrame(cm, index = [i for i in ['Zhuan','Li','Cao','Xing','Kai']],\n",
    "                  columns = [i for i in ['Zhuan','Li','Cao','Xing','Kai']])\n",
    "plt.figure(figsize = (10,7))\n",
    "sn.heatmap(df_cm, annot=True,cmap=\"Greens\",fmt=\"d\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
